Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features

Nayak, DR; Dash, R; Chang, X; Majhi, B; Bakshi, S

Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features

Nayak, DR Dash, R Chang, X

Majhi, B Bakshi, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Sustainable Computing, 2020, 5, (3), pp. 416-427
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Nayak, DR
dc.contributor.author	Dash, R
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Majhi, B
dc.contributor.author	Bakshi, S
dc.date.accessioned	2023-03-31T10:23:40Z
dc.date.available	2023-03-31T10:23:40Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE Transactions on Sustainable Computing, 2020, 5, (3), pp. 416-427
dc.identifier.issn	2377-3782
dc.identifier.issn	2377-3782
dc.identifier.uri	http://hdl.handle.net/10453/168984
dc.description.abstract	Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality of life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathological brain MR images is a more challenging task and the literature on this problem is still in its infancy. In this paper, we propose a new automated diagnosis system to classify the brain MR images into five different categories. Texture features within MR images play a significant role in accurate and efficient pathological brain detection. This work presents the extraction of such vital texture features by calculating the entropy over the curvelet subbands. Two faster and simpler strategies of fast curvelet transform are separately employed for feature extraction and the derived features are termed as FCEntF-I and FCEntF-II. The features are finally subjected to kernel extreme learning machine (K-ELM) for classification. The effectiveness of the proposed scheme is evaluated on multiclass as well as binary brain MR datasets. Comparisons with state-of-the-art methods indicate the superiority of the proposed scheme. The discriminatory potential of FCEntF-I and FCEntF-II features is found better than its counterparts.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Sustainable Computing
dc.relation.isbasedon	10.1109/TSUSC.2018.2883822
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features
dc.type	Journal Article
utslib.citation.volume	5
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T10:23:34Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	3

Abstract:

Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality of life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathological brain MR images is a more challenging task and the literature on this problem is still in its infancy. In this paper, we propose a new automated diagnosis system to classify the brain MR images into five different categories. Texture features within MR images play a significant role in accurate and efficient pathological brain detection. This work presents the extraction of such vital texture features by calculating the entropy over the curvelet subbands. Two faster and simpler strategies of fast curvelet transform are separately employed for feature extraction and the derived features are termed as FCEntF-I and FCEntF-II. The features are finally subjected to kernel extreme learning machine (K-ELM) for classification. The effectiveness of the proposed scheme is evaluated on multiclass as well as binary brain MR datasets. Comparisons with state-of-the-art methods indicate the superiority of the proposed scheme. The discriminatory potential of FCEntF-I and FCEntF-II features is found better than its counterparts.

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http://hdl.handle.net/10453/168984